In the oil and gas industry, modeling of the subsurface is typically utilized for visualization and to assist with analyzing the subsurface volume for potential locations for hydrocarbon resources. Accordingly, various methods exist for estimating the geophysical properties of the subsurface volume (e.g., information in the model domain) by analyzing the recorded measurements from receivers (e.g., information in the data domain) provided that these measured data travel from a source, then penetrate the subsurface volume represented by a subsurface model in model domain, and eventually arrive at the receivers. The measured data carries some information of the geophysical properties that may be utilized to generate the subsurface model.
The interpretation of seismic data often involves estimation of the local geometry in the measured information. Such information is useful for both interactive applications (e.g., in the form of a set of attributes) and/or in automated pattern recognition systems. The local geometry can be described by various techniques, such as estimating the tangents to peaks, correlating troughs or zero-crossings, computing the tangent planes to events. Alternatively, the local geometry may be interpreted as the local geometry of curves of equal value, such as iso-curves and/or iso-surfaces. Regardless, the local dips and azimuths (e.g., geometry) are frequently used seismic attributes. Accordingly, several methods exist to determine and utilize this information.
As a first example, one or more correlation-based methods may be utilized. See, e.g. as described in Neidell and Taner, “Semblance and other coherency measures for multichannel data”, Geophysics 36, 482-497 (1971). The document describes that the calculation of vectors should be considered local measurements. In particular, these methods work by correlating pairs of traces, which are spatially adjacent, larger spatial correlations are not naturally covered by the correlation methods.
As another example, Fomel describes how to obtain local dips from a global optimization calculation. See, e.g., Fomel, S., “Applications of plane-wave destruction filters”, Geophysics 67, 1946-1960 (2002). This reference predicts a new trace from an existing trace and the optimization minimizes the total error over all such predictions. Despite the reduction in errors, areas of poor data quality (e.g., the measurement data is poor quality) may skew predictions and therefore affect the total quality of the dip estimates. This described method is not easily adapted to ignore such areas of poor data quality.
Finally, the structure tensor method provides a smooth approximation to dips and azimuths. The method obtains gradients of the data, converts those gradients to tensors, averages the tensors in a local neighborhood, and then extracts dips and azimuths as eignevectors of those tensors. The approach is well suited under the assumption of smooth data, but it may produce inadequate results in areas of sharp discontinuity.
As the recovery of natural resources, such as hydrocarbons, rely, in part, on subsurface models, a need exists to enhance subsurface models of one or more geophysical properties. In particular, a need exists to enhance dip estimates that may work over a region of the data (not only a window of a pair of traces), that is resistant to noise in a controllable fashion, and that does not assume smoothness of the data.